Global sensitivity analysis for the Rothermel model based on high-dimensional model representation
Document Type: Journal Article
Author(s): Y. N. Liu; Y. Hussaini; G. Okten
Publication Year: 2015

Cataloging Information

  • Canada
  • fire management
  • forest management
  • fuel loading
  • fuel models
  • fuel moisture
  • Global Sensitivity Analysis
  • High-Dimensional Model Representation
  • litter
  • Monte Carlo Methods
  • rate of spread
  • Rothermel Fire Spread Model
  • slash
  • statistical analysis
  • surface fires
  • wildfires
  • wind
Record Maintained By:
Record Last Modified: June 1, 2018
FRAMES Record Number: 54248
Tall Timbers Record Number: 31913
TTRS Location Status: In-file
TTRS Call Number: Journals - C
TTRS Abstract Status: Okay, Fair use, Reproduced by permission

This bibliographic record was either created or modified by the Tall Timbers Research Station and Land Conservancy and is provided without charge to promote research and education in Fire Ecology. The E.V. Komarek Fire Ecology Database is the intellectual property of the Tall Timbers Research Station and Land Conservancy.


Rothermel's wildland surface fire spread model is widely used in North America. The model outputs depend on a number of input parameters, which can be broadly categorized as fuel model, fuel moisture, terrain, and wind parameters. Due to the inevitable presence of uncertainty in the input parameters, knowing the sensitivity of the model output to a given input parameter can be very useful for understanding and controlling the sources of parametric uncertainty. Instead of obtaining the local sensitivity indices, we perform a global sensitivity analysis that considers the synchronous changes of parameters in their respective ranges. The global sensitivity indices corresponding to different parameter groups are computed by constructing the truncated ANOVA - high dimensional model representation for the model outputs with a polynomial expansion approach. We apply global sensitivity analysis to six standard fuel models, namely short grass, tall grass, chaparral, hardwood litter, timber, and light logging slash. Our sensitivity results show similarities, as well as differences, between fuel models. For example, the sensitivities of the input parameters, i.e., fuel depth, low heat content, and wind, are large in all fuel models and as high as 85% of the total model variance in the fuel model light logging slash. On the other hand, the fuel depth explains around 40% of the total variance in the fuel model light logging slash but only 12% of the total variance in the fuel model short grass. The quantification of the importance of parameters across fuel models helps identify the parameters for which additional resources should be used to lower their uncertainty, leading to effective fire management. © 2015 by the Author(s). Published by NRC Research Press.

Liu, Y. N., Y. Hussaini, and G. Okten. 2015. Global sensitivity analysis for the Rothermel model based on high-dimensional model representation. Canadian Journal of Forest Research, v. 45, no. 11, p. 1474-1479. 10.1139/cjfr-2015-0148.